metadata
license: cc-by-nc-sa-4.0
language:
- it
- lld
Ladin-Val Badia to Italian Translation Model
Description
This model is designed for translating text between Ladin (Val Badia) and Italian. The model was developed and trained as part of the research presented in the paper titled "Rule-Based, Neural and LLM Back-Translation: Comparative Insights from a Variant of Ladin" submitted to LoResMT @ ACL 2024.
Paper
The details of the model, including its architecture, training process, and evaluation, are discussed in the paper:
License
This model is licensed under the CC BY-NC-SA 4.0 License.
Usage
To use this model for translation, you need to use the prefixes >>ita<< for translating to Italian and >>lld_Latn<< for translating to Ladin (Val Badia).
Citation
If you use this model, please cite the following paper:
@inproceedings{frontull-moser-2024-rule,
title = "Rule-Based, Neural and {LLM} Back-Translation: Comparative Insights from a Variant of {L}adin",
author = "Frontull, Samuel and
Moser, Georg",
editor = "Ojha, Atul Kr. and
Liu, Chao-hong and
Vylomova, Ekaterina and
Pirinen, Flammie and
Abbott, Jade and
Washington, Jonathan and
Oco, Nathaniel and
Malykh, Valentin and
Logacheva, Varvara and
Zhao, Xiaobing",
booktitle = "Proceedings of the The Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.loresmt-1.13",
pages = "128--138",
abstract = "This paper explores the impact of different back-translation approaches on machine translation for Ladin, specifically the Val Badia variant. Given the limited amount of parallel data available for this language (only 18k Ladin-Italian sentence pairs), we investigate the performance of a multilingual neural machine translation model fine-tuned for Ladin-Italian. In addition to the available authentic data, we synthesise further translations by using three different models: a fine-tuned neural model, a rule-based system developed specifically for this language pair, and a large language model. Our experiments show that all approaches achieve comparable translation quality in this low-resource scenario, yet round-trip translations highlight differences in model performance.",
}